What Is Data Science

The picture below gives an idea how Data Science relates to those fields:


Data Science is the practical application of all those fields (AI, ML, DL) in a business context.  “Business” here is a flexible term since it could also cover a case where you work on scientific research.  In this case your “business” is science.  Which actually is truer than you want to think about.

But whatever the context of your application is, the goals are always the same:

  • extracting insights from data,
  • predicting developments,
  • deriving the best actions for an optimal outcome,
  • or sometimes even perform those actions in an automated fashion.

As you can also see in the diagram above, Data Science covers more than the application of only those techniques.  It also covers related fields like traditional statistics and the visualization of data or results.   Finally, Data Science also includes the necessary data preparation to get the analysis done.  In fact, this is where you will spend most of your time on as a data scientist.

A more traditional definition describes a data scientist as somebody with programming skills, statistical knowledge, and business understanding. And while this indeed is a skill mix which allows you to do the job of a data scientist, this definition falls a bit short.  Others realized this as well which led to a battle of Venn diagrams.

The problem is that people can be good data scientists even if they do not write a single line of code. And other data scientists can create great predictive models with the help of the right tools.  But without a deeper understanding of statistics.  So the “unicorn” data scientist (who can master all the skills at the same time) is not only overpaid and hard to find.  It might also be unnecessary.

For this reason, I like the definition above more which focuses on the “what” and less on the “how”.  Data scientists are people who apply all those analytical techniques and the necessary data preparation in the context of a business application.  The tools do not matter to me as long as the results are correct and reliable.

 

What is Artificial Intelligence, Machine Learning, and Deep Learning?

This post should help to understand the differences and relationships of those fields. Let’s get started with the following picture. It explains the three terms artificial intelligence, machine learning, and deep learning:

Artificial Intelligence is covering anything which enables computers to behave like a human.  Think of the famous – although a bit outdated – Turing test to determine if this is the case or not.  If you talk to Siri on your phone and get an answer, this is close already.  Automatic trading systems using machine learning to be more adaptive would also already fall into this category.

Machine Learning is the subset of Artificial Intelligence which deals with the extraction of patterns from data sets. This means that the machine can find rules for optimal behavior but also can adapt to changes in the world. Many of the involved algorithms are known since decades and sometimes even centuries. But thanks to the advances in computer science as well as parallel computing they can now scale up to massive data volumes.

Deep Learning is a specific class of Machine Learning algorithms which are using complex neural networks.  In a sense, it is a group of related techniques like the group of “decision trees” or “support vector machines”.  But thanks to the advances in parallel computing they got quite a bit of hype recently which is why I broke them out here. As you can see, deep learning is a subset of methods from machine learning.  When somebody explains that deep learning is “radically different from machine learning“, they are wrong.  But if you would like to get a BS-free view on deep learning, check out this webinar I did some time ago.

But if Machine Learning is only a subset of Artificial Intelligence, what else is part of this field?  Below is a summary of the most important research areas and methods for each of the three groups:

  • Artificial Intelligence: Machine Learning (duh!), planning, natural language understanding, language synthesis, computer vision, robotics, sensor analysis, optimization & simulation, among others.
  • Machine Learning: Deep Learning (another duh!), support vector machines, decision trees, Bayes learning, k-means clustering, association rule learning, regression, and many more.
  • Deep Learning: artificial neural networks, convolutional neural networks, recursive neural networks, long short-term memory, deep belief networks, and many more.

As you can see, there are dozens of techniques in each of those fields. And researchers generate new algorithms on a weekly basis.  Those algorithms might be complex.